Summary Conclusions: Computation of Minimum Volume Covering Ellipsoids*

We present a practical algorithm for computing the minimum volume n-dimensional ellipsoid that must contain m given points a₁,..., am ∈ Rn. This convex constrained problem arises in a variety of applied computational settings, particularly in data mining and robust statistics. Its structure makes...

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Main Authors: Sun, Peng, Freund, Robert M.
Format: Article
Language:en_US
Published: 2003
Subjects:
Online Access:http://hdl.handle.net/1721.1/3896
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author Sun, Peng
Freund, Robert M.
author_facet Sun, Peng
Freund, Robert M.
author_sort Sun, Peng
collection MIT
description We present a practical algorithm for computing the minimum volume n-dimensional ellipsoid that must contain m given points a₁,..., am ∈ Rn. This convex constrained problem arises in a variety of applied computational settings, particularly in data mining and robust statistics. Its structure makes it particularly amenable to solution by interior-point methods, and it has been the subject of much theoretical complexity analysis. Here we focus on computation. We present a combined interior-point and active-set method for solving this problem. Our computational results demonstrate that our method solves very large problem instances (m = 30,000 and n = 30) to a high degree of accuracy in under 30 seconds on a personal computer.
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spelling mit-1721.1/38962019-04-12T08:36:43Z Summary Conclusions: Computation of Minimum Volume Covering Ellipsoids* Sun, Peng Freund, Robert M. ellipsoid Newton’s method interior-point method barrier method active set semidefinite program data mining robust statistics clustering analysis We present a practical algorithm for computing the minimum volume n-dimensional ellipsoid that must contain m given points a₁,..., am ∈ Rn. This convex constrained problem arises in a variety of applied computational settings, particularly in data mining and robust statistics. Its structure makes it particularly amenable to solution by interior-point methods, and it has been the subject of much theoretical complexity analysis. Here we focus on computation. We present a combined interior-point and active-set method for solving this problem. Our computational results demonstrate that our method solves very large problem instances (m = 30,000 and n = 30) to a high degree of accuracy in under 30 seconds on a personal computer. Singapore-MIT Alliance (SMA) 2003-12-14T23:22:42Z 2003-12-14T23:22:42Z 2004-01 Article http://hdl.handle.net/1721.1/3896 en_US High Performance Computation for Engineered Systems (HPCES); 192207 bytes application/pdf application/pdf
spellingShingle ellipsoid
Newton’s method
interior-point method
barrier method
active set
semidefinite program
data mining
robust statistics
clustering analysis
Sun, Peng
Freund, Robert M.
Summary Conclusions: Computation of Minimum Volume Covering Ellipsoids*
title Summary Conclusions: Computation of Minimum Volume Covering Ellipsoids*
title_full Summary Conclusions: Computation of Minimum Volume Covering Ellipsoids*
title_fullStr Summary Conclusions: Computation of Minimum Volume Covering Ellipsoids*
title_full_unstemmed Summary Conclusions: Computation of Minimum Volume Covering Ellipsoids*
title_short Summary Conclusions: Computation of Minimum Volume Covering Ellipsoids*
title_sort summary conclusions computation of minimum volume covering ellipsoids
topic ellipsoid
Newton’s method
interior-point method
barrier method
active set
semidefinite program
data mining
robust statistics
clustering analysis
url http://hdl.handle.net/1721.1/3896
work_keys_str_mv AT sunpeng summaryconclusionscomputationofminimumvolumecoveringellipsoids
AT freundrobertm summaryconclusionscomputationofminimumvolumecoveringellipsoids